评鉴平台的供求效应

Gregory Lewis, G. Zervas
{"title":"评鉴平台的供求效应","authors":"Gregory Lewis, G. Zervas","doi":"10.2139/ssrn.3468278","DOIUrl":null,"url":null,"abstract":"Review platforms such as Yelp and TripAdvisor aggregate crowd-sourced information about users' experiences with products and services. We analyze their impact on the hotel industry using a panel of hotel prices, sales and reviews from five US states over a 10-year period from 2005--2014. Both hotel demand and prices are positively correlated with their average ratings on TripAdvisor, Expedia and Hotels.com, and such correlations have grown over our sample period from a statistical zero in the base year to a substantial level today: a hotel rated one star higher on all the platforms on average has 25% higher demand, and charges 9% more. We argue that the price increases are due to a combination of revenue management and re-pricing: increased demand from higher ratings shifts hotels along an upward sloping supply curve, and also causes small but significant changes in the supply curve itself. A natural experiment in our data that caused abrupt changes in the ratings of some hotels but not others, suggests that these associations are causal. Building on this causal interpretation, we estimate heterogenous treatment effects, showing that the impact of review platforms on hotels varies by organization form and hotel class. Specifically, we show that independent hotels that had little outside reputation prior to the entry of review platforms stand to gain more than chains.","PeriodicalId":416173,"journal":{"name":"Proceedings of the 2019 ACM Conference on Economics and Computation","volume":"8 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"The Supply and Demand Effects of Review Platforms\",\"authors\":\"Gregory Lewis, G. Zervas\",\"doi\":\"10.2139/ssrn.3468278\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Review platforms such as Yelp and TripAdvisor aggregate crowd-sourced information about users' experiences with products and services. We analyze their impact on the hotel industry using a panel of hotel prices, sales and reviews from five US states over a 10-year period from 2005--2014. Both hotel demand and prices are positively correlated with their average ratings on TripAdvisor, Expedia and Hotels.com, and such correlations have grown over our sample period from a statistical zero in the base year to a substantial level today: a hotel rated one star higher on all the platforms on average has 25% higher demand, and charges 9% more. We argue that the price increases are due to a combination of revenue management and re-pricing: increased demand from higher ratings shifts hotels along an upward sloping supply curve, and also causes small but significant changes in the supply curve itself. A natural experiment in our data that caused abrupt changes in the ratings of some hotels but not others, suggests that these associations are causal. Building on this causal interpretation, we estimate heterogenous treatment effects, showing that the impact of review platforms on hotels varies by organization form and hotel class. Specifically, we show that independent hotels that had little outside reputation prior to the entry of review platforms stand to gain more than chains.\",\"PeriodicalId\":416173,\"journal\":{\"name\":\"Proceedings of the 2019 ACM Conference on Economics and Computation\",\"volume\":\"8 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 ACM Conference on Economics and Computation\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2139/ssrn.3468278\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 ACM Conference on Economics and Computation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2139/ssrn.3468278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

点评平台如Yelp和TripAdvisor聚集了用户对产品和服务体验的信息。我们利用2005年至2014年10年间美国5个州的酒店价格、销售和评论来分析它们对酒店业的影响。酒店的需求和价格都与它们在TripAdvisor、Expedia和Hotels.com上的平均评分呈正相关,而且在我们的样本期内,这种相关性已经从基准年的统计为零增长到今天的显著水平:在所有平台上得分高1星的酒店,需求平均高出25%,收费高出9%。我们认为,价格上涨是由于收入管理和重新定价的结合:高评级带来的需求增加使酒店沿着一条向上倾斜的供应曲线移动,同时也导致供应曲线本身发生微小但显著的变化。在我们的数据中进行了一个自然实验,导致一些酒店的评级突然发生变化,而另一些则没有,这表明这些关联是因果关系。在这一因果解释的基础上,我们估计了异质性处理效应,表明点评平台对酒店的影响因组织形式和酒店类别而异。具体来说,我们表明,在点评平台进入之前几乎没有外部声誉的独立酒店将比连锁酒店获得更多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Supply and Demand Effects of Review Platforms
Review platforms such as Yelp and TripAdvisor aggregate crowd-sourced information about users' experiences with products and services. We analyze their impact on the hotel industry using a panel of hotel prices, sales and reviews from five US states over a 10-year period from 2005--2014. Both hotel demand and prices are positively correlated with their average ratings on TripAdvisor, Expedia and Hotels.com, and such correlations have grown over our sample period from a statistical zero in the base year to a substantial level today: a hotel rated one star higher on all the platforms on average has 25% higher demand, and charges 9% more. We argue that the price increases are due to a combination of revenue management and re-pricing: increased demand from higher ratings shifts hotels along an upward sloping supply curve, and also causes small but significant changes in the supply curve itself. A natural experiment in our data that caused abrupt changes in the ratings of some hotels but not others, suggests that these associations are causal. Building on this causal interpretation, we estimate heterogenous treatment effects, showing that the impact of review platforms on hotels varies by organization form and hotel class. Specifically, we show that independent hotels that had little outside reputation prior to the entry of review platforms stand to gain more than chains.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信